AIRS Data Assimilation at SPoRT Brad Zavodsky and Will McCarty (UAH) - PowerPoint PPT Presentation

AIRS Data Assimilation at SPoRT Brad Zavodsky and Will McCarty (UAH) Shih-hung Chou and Gary Jedlovec (MSFC) AIRS Science Team Meeting Greenbelt, MD October 10, 2007 1 transitioning unique NASA data and research technologies to the NWS

Outline Motivation: Use of AIRS measurements within a data assimilation system can potentially provide better atmospheric representation—particularly over data void regions—and improve short-term weather forecasts ♦ SPoRT AIRS Assimilation focuses on short-term regional forecasts—compliments work at JCSDA ♦ Profile Assimilation (B. Zavodsky) • Motivation and review of previous case study work • Design of experiment for month-long statistics • Results from month-long statistics ♦ Direct Radiance Assimilation (W. McCarty) • Channel selection and assimilation cycle • Results of case study ♦ SPoRT AIRS DA work presented Sept. 24 and 25 at EUMETSAT/AMS Satellite Conference in Amsterdam, The Netherlands 2 transitioning unique NASA data and research technologies to the NWS

Profile Assimilation Introduction ♦ Assimilation of AIRS profiles may benefit regional centers that are influenced by data sparse areas but are not equipped to handle radiance assimilation • Melbourne and Miami NWS WFOs ♦ Previous work at SPoRT has focused on Nov. 20-22, 2005 case study • Found that AIRS profiles have positive impact on analyses by shifting large-scale model first-guess towards rawinsonde observations • AIRS-updated initial conditions showed positive impact in temperature, mixing ratio, and 6-hr cumulative precipitation at most forecast times ♦ More days needed to be run to find new case studies and to obtain a more robust set of cumulative statistics of forecast impact • 33 days of model runs from 17 January to 22 February 2007 were run (missing initial conditions for 3-5 February and 11 February) • These results are shown herein 3 transitioning unique NASA data and research technologies to the NWS

Experiment Design ♦ L2 Version 5 temperature and moisture profiles assimilated over land and water with quality control using AIRS Time: AIRS Time: P Best value in each profile 0800 UTC 0800 UTC • Eastern and central CONUS swathes combined into one swath; assimilation time is mean of the two overpasses • Only night time overpasses used ♦ 12-km WRF initialized at 0000 UTC on each forecast Valid: Valid: 0836-0848 UTC 0700-0712 UTC date using 40-km ETA/NAM; ADAS to assimilate profiles ♦ Results of the 33 days of model runs are validated using sensible weather parameters compared to observations • Temperature and mixing ratio verified with 50 radiosondes east of 105 o W • 6-hr cumulative precipitation verified with NCEP Stage IV data east of 105 o W mapped to WRF grid 4 transitioning unique NASA data and research technologies to the NWS

Results: 36 Hour Forecast Impact ♦ AIRS reduces temperature bias at most levels by ≈ 0.3 o C in lower and upper levels ♦ AIRS changes low and mid-level moisture by as much as 5% at some levels ♦ Temperature and moisture adjustments made without large increases to RMS error 0.3 1.5 CNTL AIRS 0.25 1.25 ♦ 6-hr cumulative precipitation improves with Equitable Threat Score 0.2 1 inclusion of AIRS profiles Bias Score • Larger ETS (bars) for AIRS runs indicates 0.15 0.75 improvement in predicted precipitation location 0.1 0.5 and amount • Bias scores (lines) closer to 1.0 for AIRS 0.05 0.25 suggest improvement in coverage of precipitation features 0 0 0.254 2.540 6.370 12.70 19.05 Minimum Precipitation Threshold (mm) 5 transitioning unique NASA data and research technologies to the NWS

Outline Motivation: Use of AIRS measurements within a data assimilation system can potentially provide better atmospheric representation—particularly over data void regions—and improve short-term weather forecasts ♦ SPoRT AIRS Assimilation focuses on short-term regional forecasts—compliments work at JCSDA ♦ Profile Assimilation (B. Zavodsky) • Motivation and review of previous work • Design of experiment for month-long statistics • Results from month-long statistics ♦ Direct Radiance Assimilation (W. McCarty) • Channel selection and assimilation cycle • Results of case study ♦ SPoRT AIRS DA work presented Sept. 24 and 25 at EUMETSAT/AMS Satellite Conference in Amsterdam, The Netherlands 6 transitioning unique NASA data and research technologies to the NWS

Radiance Assimilation Introduction ♦ In the NCEP Global Data Assimilation System (GDAS), AIRS has already been shown to have a significant impact in both northern and southern hemisphere global forecasts (Le Marshall et al. 2006) ♦ Previous work focused on preparation of AIRS radiances for data assimilation • CO 2 Sorting Technique can detect clouds and determine uncontaminated channels in hyperspectral data to increase the number of usable channels over a masking approach ♦ The proper use and assessment of these measurements within a regional system—such as the North American Model (NAM) Data Assimilation System (NDAS)—has yet to be fully assessed • Considerations of the proper utilization of AIRS data within the pseudo- operational NDAS environment and a preliminary look at their impact are investigated herein 7 transitioning unique NASA data and research technologies to the NWS

Channel Selection for Regional Assimilation ♦ Operationally, NCEP GFS uses 151 T channels of the 281 channel subset ♦ Limitations to using a regional model: • lower P top (2 hPa; red line) Pressure (hPa) • O 3 not used in regional model q ♦ No shortwave (< 5 µ m) channels are used ♦ Plots show profile normalized Jacobians of dT each constituent: b * 0 . 1 q i dq i O 3 ♦ Green hashes denote 151 GDAS channels ♦ Red hashes denote 103 regional channels ♦ No additional channels in regional subset that are not used in global analysis - 0 + 8 transitioning unique NASA data and research technologies to the NWS

Assimilation Cycle 48hr 48hr 48hr 48hr 48hr 00 03 12 15 18 21 00 06 09 Time (UTC) ♦ All NCEP operational observations are assimilated every 3 hours (± 1.5 hrs) for the NOAIRS runs; AIRS radiances are the only difference between NOAIRS and AIRS runs ♦ A two-week spin-up period to propagate the impact of the AIRS measurements through the analysis and allow bias corrections to stabilize ♦ Gridpoint Statistical Interpolation (GSI) and the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM) used as analysis and model systems 9 transitioning unique NASA data and research technologies to the NWS

Initial Results ♦ 48-hr forecast valid at 0000 UTC on 11 April 2007 ♦ 500 hPa height anomalies for control (NOAIRS; blue) and control+AIRS (AIRS; red); corresponding NDAS analysis in black B ♦ Solid contours correspond to troughs; dashed contours correspond to ridges Height Anomaly A Analysis AIRS NOAIRS Pressure (hPa) ♦ A: model domain (dashed lines) B ♦ B: subdomain characterisized by conventional obs in analysis (solid lines) A ♦ Both height anomaly correlation (R) and standard deviation ( σ ) show significant improvement throughout the troposphere σ R 10 transitioning unique NASA data and research technologies to the NWS

Summary ♦ SPoRT AIRS Assimilation focuses on short-term regional forecasts—compliments work at JCSDA ♦ Profile Assimilation Conclusions/Future Work • For 33 days of model runs in late Jan./early Feb.  Biases are reduced in temperature and mixing ratio at most levels  6-hr cumulative precipitation coverage and forecast accuracy improve • Further analysis of individual days from case study to determine where AIRS provides most added value; migrate to 3DVAR ♦ Direct Radiance Assimilation Conclusions/Future Work • Limitations in use of AIRS radiances in regional NDAS reduced the number of usable channels by 17% relative to the 281 subset but still retained 37% of the channels overall • An initial case study shows statistically significant forecast improvement throughout the entire model domain due to the assimilation of AIRS data • Further investigate determination of cloud contamination using CO 2 sorting technique; further investigate use of AIRS radiances over a longer set of studies 11 transitioning unique NASA data and research technologies to the NWS

Questions? Suggestions? Comments? 12 transitioning unique NASA data and research technologies to the NWS